---
res:
  bibo_abstract:
  - "This paper evaluates the forecasting performance of an expanded class of (semi-)parametric
    \r\nGARCH models belonging to the EGARCH family (EGF), including recently introduced
    long  \r\nand short memory specifications and their semiparametric extensions.
    The semiparametric \r\nvariants employ a multiplicative volatility decomposition
    into conditional and slowly varying \r\nunconditional components, where the latter
    is estimated via a data-driven local polynomial \r\nsmoother to accommodate non-stationarities
    commonly observed in financial time series. Based \r\non the revised Basel Committee
    framework for market-risk assessment, all models are capable \r\nof producing
    rolling one-day-ahead forecasts for Value at Risk (VaR) and Expected Shortfall
    \r\n(ES) under a wide range of symmetric and skewed innovation distributions.
    Their forecasting \r\naccuracy is examined using the regulatory traffic light
    tests for VaR and the recently developed \r\nES-specific traffic light procedure,
    complemented by the regulatory loss function. In addition, \r\nmodel selection
    incorporates both a recently proposed corrected firm-oriented loss function that
    \r\naccounts for opportunity costs and the Weighted Absolute Deviation (WAD) criterion.
    The \r\nempirical comparison demonstrates that (semiparametric) long memory GARCH
    models - \r\nparticularly those combining fractional dynamics with nonparametric
    scale adjustments - can \r\nserve as valuable alternatives to traditional parametric
    short memory models, offering more \r\nstable volatility estimates and improved
    tail-risk forecasts for practical risk management \r\napplications.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Dominik Christian
      foaf_name: Hanke, Dominik Christian
      foaf_surname: Hanke
      foaf_workInfoHomepage: http://www.librecat.org/personId=63677
  - foaf_Person:
      foaf_givenName: André
      foaf_name: Uhde, André
      foaf_surname: Uhde
      foaf_workInfoHomepage: http://www.librecat.org/personId=36049
  - foaf_Person:
      foaf_givenName: Yuanhua
      foaf_name: Feng, Yuanhua
      foaf_surname: Feng
      foaf_workInfoHomepage: http://www.librecat.org/personId=20760
  dct_date: 2026^xs_gYear
  dct_language: eng
  dct_subject:
  - semiparametric GARCH extension
  - data-driven local polynomial smoother
  - long  memory
  - GARCH models
  - Value at Risk
  - Expected Shortfall
  - traffic light test
  - backtesting
  - Basel  III
  - market risk
  dct_title: Application of Novel Exponential (Semi-)Parametric Short and Long  Memory
    GARCH Models under Regulatory Requirements of Basel III@
...